{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AoG0IhkBrcw/Wm6bOJcj6PABoG+Yx5JB1LkHa85iLAKAJzGOoUHwuQdqFPmnOAX0PANqO\nYCgo9EKf1PeQ1nlNrQJAEwiGgkIv9PMb52auj0StAkCTCIaCsiyEF7/Q7zv3NC0dWk6tDWQNGwCo\nAsFQUJatNOMX+qVDy9q7Z3vqe2ZddRUAqkAwlGDWwnahF3r2bQbQJIar1oTOZABNY7hqTlVdwLMs\nl014AGgDgiGiydFAjEQC0BYsiRHR5PpGrK0EoC0Ihogy1jfKu9Q2aysBaAs6n2OKtPMXbQ6ijwFA\nleh8zqnInspFJ6axnzOANqApqUQ0BwHoA2oMJWJiGoA+IBhKRnMQgK6jKQkAMIFgAABMIBgAABNK\n62Mws3WSTpf0ZEmPlDQn6V5Jt0q63t1/WNa5msAcAwBDUTgYzGyzpD+WdIGkk1KetmpmX5D0Vne/\nqug568Y6RgCGpFBTkpmdJ+m/Jb1W6aGwdp6zJH3CzD5uZg8vct6yzVrGgnWMAAxJ7hqDme2VdFmO\nl75Q0pfM7Kw2NC9lqQ2woxqAIckVDGb2fEnvSHjoZknvknSjpHskbZP0AkkvlXRM5HmnSrrSzJ7p\n7it5ylCWactYRPsVmLgGYCiCg8HMHiHpCkkWe+hNki72yVX5rpf0YTN7o6RPS9oaeexMSX8q6a9D\ny1CmtNpAUk1i2j7NANAXeWoMfy5pc+zYpe7+urQXuPs3zewZkr4u6cTIQxeb2Xvc/Y4c5ShF2jIW\nRRfEA4CuCup8NrNNki6MHb5F0utnvdbdb5P0mtjh4yS9MqQMVdi5dV5792yfuPCzIB6AoQodlfRb\nkjbGjl3q7vdlfP0VkuK1g98zs/WB5ajcWk3ionN2MDwVwKCENiW9JPbzfZLen/XF7r5iZu+VFG12\nerSk3ZK+EFiWyrEgHoAhylxjMLPjNeowjvqyu98beM7PJBw7J/A9AAAVCWlK2qnJIaeS9KUc57xe\n0nLs2K4c7xMs737MADAkIU1JpyccWww9obvfb2YHJD1lxnuXimUtACCbkBpD0iD+/8l53ttiP28y\nsxMTn1kSlrUAgGxCgmFLwrH4BT6rpNdtTThWmvjw0/mNczQrAUCCkKak+KS2ZXf/Uc7z3pnh/UsV\nncg2v3FOl1x1gGYlAEgQUmOIN/VknbuQJOm1la+4ujaRbenQMs1KAJAiJBiOjf18f4HzJgXDQwq8\nXxBmNQNAupCmpPhz40NOQ/ws4Vh8KOwEM7tQ4+U4tmxJ6u7ILm19JABAWDDEl8eeK3DepNrB4Wkv\ncPd3S3q3JC0sLPi052bBrGYASBbSlBS/y483LYU4LsP7AwAaEBIM8RFISRf3rJJem3eEEwCgRCHB\ncFfs57kCezf/XMIxhgYBQAuEBEOZk9KSeo8P5nwvAECJQoLh2wnHygqGuwtMlgMAlCgkGL6acGxn\n6AnN7FhJp8UOfy30fQAA1QgZrrqo0ZDS6HyDX85xzqfp6KGu14a8weLi4l1mlrfpabOO7i9B//B7\nHgZ+z2EytfJkDgZ3/6mZfUXSMyKHn25mDwvcrOe5Ccc+F/B6uftJIc+PMrP97r6Q9/XoBn7Pw8Dv\nuRqhez5/JPbzcZJ+O+uLzWyDpAtih++Q9B+B5QAAVCQ0GD6oo9c5evW43yCL8yU9KnbsH939SGA5\nAAAVCQoGd79b0ntih7dL+ptZrzWzx0l6S+zwfZLeHlKGEry75vOhGfyeh4HfcwXMPWzZITPbJOlm\nSfElSf9W0us94Q3N7ImSPiVpW+yhv3D3S4IKAACoVHAwSJKZvVDSlZIs9tBNkt4p6UZJP9aoB/xc\nSb+p5JFIz3D3qYvnAQDqlSsYJMnMXqH8zUA3SXqmu/8g5+sBABUJ7Xx+gLv/vaSXKnzxu09K2l1n\nKJjZo8zs7Wb2bTO738zuNLNPmNmz6ioDqmNmW8zsVePf6W1m9jMz+7GZfc3M3mRmj266jCifmT3U\nzG43Mx//Ob/pMvVF7hrDA29gdpKk12o04iht32aX9EVJb3X3fyl0wkBm9kuS/l0P9oncK+mhGoWi\nS7rY3d9UZ5lQnvGghoOabNa8V9LxktaPf16SdJ67X11z8VAhM3ubpFdGDl3g7lc0VJxeKRwMD7yR\n2TpJZ0h6kkarpx6j0f+g35F0XRPNRmZ2nKRvatTX8VVJv+PuB8zsYZL2Sfqj8VOf6+5Bk+zQDma2\nTdKtGg1uuELS5919yczmJD1L0uWSHq/Rf4s73P2OZkqKMpnZGZKul7Rfo9UUJIKhNKUFQxuZ2ask\nXSrpJ5Ke4O7fiz3+MUm/JukGdw9e9wnNM7MTJW1z98T1tszsCRrdFBwr6Q3u/pd1lg/lG9+EXifp\nKZKeKumG8UMEQ0ly9zF0xMvGf38wHgpjbx7/fcb4AoKOcfd70kJh/PhNenAtLsK/H/5Q0oKkf3D3\npMU9UVBvg8HMTtCDF4LPpjztWkn3jP99duWFQlPWNoFaP/VZaD0zO1nSX0m6U9KfNVyc3uptMEh6\noh7skDyQ9AR3X9Vosp4knVpHoVCv8fpcu8c/3thkWVCKd0g6QdJr3P2eWU9GPn0OhugQxe9Ped7a\nYwxp7Ke9Gq3PtSrpfQ2XBQWY2QskvUjSNe7+/qbL02d9DobjI/+OL/wXdWj890MrLAsaMB6q/Mbx\nj5e5e2LNEe1nZsdLukyjPWH2Nlyc3utzMMSX68CAjCe1XSlpo0abTP1JsyVCQZdotCXwpe7+jaYL\n03d9DoafRP593JTnbUx4PjrMzB6h0eZPj5f0LUnPd/f7my0V8jKz0zWayHa7RgGBioVs7dk10X6F\nx+jBTua4x4z//t9qi4M6jOc1fFajiZa3SXq2u9/ZbKlQ0Ns1GlH2eklmZmnNvg8ZP7bq7odSnoMM\n+lxjuEmjJS8k6bSkJ4wnyuwY/0j1tOPG7dCf0miM+x0ahcJtzZYKJVjbp/h9Gq3aHP+z5p3jn/l/\nuaDeBoO7/1ij6fKS9JyUpz1N0onjf3++8kKhMuPlTz4h6UyN5i08292/1WypgG7qbTCMfXD898tS\nVth8zfjvRXdPa2pCy43XRfpnSXs0Wu33HEYg9Ye7b3N3S/sTeeoF42PbmiprX/Q9GN6l0cqbJ0i6\nysxOlUazos3s7yS9ePy8ixsqHwoys/Ua3QA8T6NmhF9x9xumvwrANH3ufJa732dmv6pRM9EZkg6Y\nWdKy26ys2l27JZ03/vcxkq40Sx2pfLu7P7WWUgEd1utgkCR3/5qZPUnS6zTaZvRkjdqgr9doTDR9\nC90WrfUeO/6ThiGrQAa9XnYbABCu730MAIBABAMAYALBAACYQDAAACYQDACACQQDAGACwQAAmEAw\nAAAmEAwAgAkEAwBgAsEAAJjw/5w+hUU6jLshAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fa7160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ysr6jMcZw7YmD/D1MRKTT8afF0Pi3/LbMGxHfgvcXEREH+BMMjUcgNfXl3lJN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\nYFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11308b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vvgtAfn4+y5Yt47jjjnMOmDZNS2+KSEIoMGQBay2XX345r776qn/fk08+yciR\nI9sPCjcZrbpazUoiEhUFhiwwbdo0Fgfc2O+++24mTJgQfFBHk9F8zUoKDiISAQWGDDd37lzuvfde\n//YVV1zBLbfcsveBbpPUAqlZSUQipMCQwVatWsXVV1/t3x45ciRz5szBGLP3wYGT1MJR7iMRiYAC\nQ4b6+9//ztixY/F4nHnNw/ffn+fWraOgW7fwfQa+tRbCBQflPhKRCCgwZKCamhpGjhxJQ0MDAEP6\n9uX3W7bQo6YGrHX6DMaNc9JXuAUJ5T4SkTgoMGSCgIlp9RUVnHvCCXz55ZcA9O7dm/8pLORbTU3B\nn/Gto+HWsazcRyISBy3Uk24BE9PagPOAP3jfKiwsZPXq1Zx6+untgSAcLaojIp3QQj3ZwjsxzeKs\nqfCHgLfmz5/PqaeeGlnfgDqWRSRBFBjSzXtDfwR4LGD3bcD48eOdjc6GooI6lkUkYRQY0q2iglXA\nlIBdPwbuDLzRhw5FDR2uqo5lEUkgBYY0e+/KK/kx+BfbOQ5YWFyMueee4AN9Q1GthWeeUceyiCSN\nsqum0bZt2zhv7lx2ebcrgecHDaJo5syOb/RaVEdEkkg1hnhEsv5BmGOam5sZPXo01dXVAPTs2ZPf\nrV9Pv5oa3fRFJK1UY4hVJOsfhDnGWsukl1/mrbfeAiAvL4/nnnuOww8/PMW/hIjI3jSPIVZDhjg3\n+lCB8wnCHPPL3r25eccO//aDDz7IlClT9jpORCSRNI8h2cLNGwjc73LMi8AvAoLCxIkTmTx5coIL\nJyISOwWGWIWbNxC4P+SY94AqwFdH+0H37jy2YAFm//21VoKIZAwFhli5TTozJnjFtIBjvgbOB/8I\npP2N4bctLXQDLaQjIhlFgSFWbpPOQhPbAcybR2tFBRcBG7wf7WkML1pLeeD53BbSiWTUk4hIgikw\nxCNw/YPQTnzvjd7++79z3YgR/Mm72wBLrGWY2/kC+yR8I5qqq9tTbatWISIpoMCQCB10RD/++OM8\n8cQT/l334GRQdRXYJ+FNrhdEy3OKSAooMCRCmI7oP/brx3XXXeffvgS4Odw5QvMdRTLqSUQkCRQY\nEsGlI/qzoiJ+1NDAnj17ABgOLMBpStqLW76jSEY9iYgkgQJDIoSsmLZr8GBGlZdTu8sZg7Tffvux\ncuBAit0+65sQF5oGQ8tzikiaKDCEE+2IIG9HtKetjfHDh/P+pk0AdOvWjZUrVzLovvsiu9H7rjtu\nHBQXQ1mZsqiKSGpZa7Pudcwxx9ikWrTI2pISa53xQM6rpMTZ34np06dbnDlsFrALFy4MPm9lpbXG\nOD9DzxfHdUVEOgOssRHcY5UXl7rqAAALcElEQVQryU0keZBcrFixggsvvNC/PXnyZGbNmpX064qI\nREK5kuIRw4ig9957r30pTuCMoiLuf+ih6CamaSSSiGQABQY3UY4Iqq2tZdSoUTQ0NABwgDEsbW52\ncppHMzFNI5FEJAMoMLiJYkRQW1sbF198MZ9//jngpLt4wVpKAw+KdGKaRiKJSAZQYHATMvy0oxFB\nN910E6+99pp/+5lI0l0k4LoiIsmizuc4PP3000yYMMG/feedd3L7U0+pA1lEMpI6n5Psb3/7G1dc\ncYV/+8ILL+TWW29Vc5CIZD0Fhhh88cUXjBkzhtbWVgCGDRvG008/TV5enpqDRCTrFaS7ANmmubmZ\n0aNH8+WXXwJQWlrKCy+8QM+ePdsPqqpSIBCRrKUaQxSstVx55pm88847AOQDyyZN4oADDkhvwURE\nEihhNQZjTB5wFHA40A/oBtQDnwHvWGu/StS10mX2+PH8+s9/9m8/AJz+8MMwbJhqCCLSZcRdYzDG\nlBtj7gO2AGuBp4FfAncDDwO/B7YYY/5ojBkZ7/WSqoPEea+88go3Llrk354AXAdaPEdEupy4agzG\nmAuBJ4E+nRyaB5wCnGKMeRG41Fq7I55rJ5xvKU3fqmkB6zZ/euyxjB07Fo/30GOBxwlYW0EpK0Sk\nC4k5MBhjrgbmxPDR84E3jTGnZFTzUpilNOunTuV8j4e6ujoABgArgO6BxyllhYh0ITEFBmPMucAj\nLm99BDwBvA/sBIbgLHE8FigMOO5Q4HljzMnW2rZYypBwLk/9HuAnNTV86N3uDqwE+gcepDkKItLF\nRN3HYIwpxelHCF2lciZwiLV2lrX2FWvtO9baZdbaccCRQOh04OOBX8RQ5uRweeq/DfhdwPYC4HuB\nB2iOgoh0QbF0Pt8GlIfsm2WtnWrD5New1v4T+AFOLSLQLcaY/WIoQ+KFzFh+Drgn4O2fA0G3f2Pc\nl+QUEclyUQUGY0wZMClk9ydAp8NyrLUbgZtCdhcD10dThqQJmLH8v8AE014hOofgIAGoX0FEuqxo\nawyXACGJgJhlrW2K8PNP4wxrDXSpMSY/ynIkR1UVX7z1FhcMGECzt/Jz8IABLCkuJqiA6lcQkS4s\n2sBwUch2E7DI7UA33o7mhSG7+wMnRFmOpPClu/jiiy8A6NOnDy++/jr7Pvmkch+JSM6IODAYY3rg\ndBgHestaWx/lNf/gsu/MKM8RH5eJbNZaJk6c2J7uIj+fZcuWceCBBzpBYMMG8HjUryAiXV40w1WP\nIXjIKcCbMVzzHaAVJ2WGz7ExnCc2YSay3fvCCyxZvtx/2IMPPsgZZ5yRsmKJiGSKaJqSjnLZtzba\nC1prm4EPIjh3crhMZFvZ2Mi0gKAwadIkrr322pQVSUQkk0QTGIa67NsQ43VDZ5OVGWP2jfFcUV45\n+NLvAj8J2D7lkEOYM2cOxoRO0xARyQ3RBAa38ZmxJgly+1xljOeKTsAw0y9xpmX76g/fBn6zYQOF\ny5alpCgiIpkomsAQOqmtNY5EeFsjOH9yeCeyNQKjgE3e3fvizHIua2pStlQRyWnRBIbQpp5I5y64\ncfts7zjOF7mqKjxz53JZSQn/692VDywDDvEdo2ypIpLDogkMRSHbzXFc1y0wdHfZlxTTP/6Y5QEd\n0I8QMl5Ws5pFJIdFExhCh7a2xnHdFpd9oUNhgxhjJhlj1hhj1nz1VezZunfv3s2fA1Zhu7aggKsC\nD9CsZhHJcdEEhtD02N1cj4qMW+1gd0cfsNbOs9YOt9YO79u3b8wXLiwsZPXq1Vx++eWcffbZPLhg\ngWY1i4gEiGaCW+hTfmjTUjSKIzh/0nTr1o358+fT0tJCQVERjB+fqkuLiGS8aGoMoSOQ3G7ukXL7\nbEqX+jTGUFQUT2wTEemaogkMX4dsdzPGxDqS6Fsu+2pjPJeIiCRQNIEhkZPS3Ib9hK7wJiIiaRBN\nYPjUZV+iAkNtHJPlREQkgaIJDP9w2XdMtBc0xhQBh4XsXhfteUREJDmiCQxr2XtI6YkxXPP77D3U\n9W8xnEdERJIg4sBgrW0A/hqy+zhjTK8or3mWy76XozyHiIgkSbRLe/4mZLuY4KzVHTLGFAATQnZv\nAf4SZTlERCRJjPUueh/RwcaUATUEz0P4BDjcuwBPZ5//D+DJkN33WWt/EXEhnPN8RWJGMZWz9zBc\n6Rr0t+3a9PeNTaW1ttPUEVEFBgBjzMNA6PJmD1prb+zkc4OB9wjO0toEfNta+2VUhUgQY8waa+3w\ndFxbkkt/265Nf9/kirYpCeBO9p6MdoMx5h4TZtkzY8whwBvsnbp7ZrqCgoiIuIsmVxIA1tpaY8zl\nwPNAYCCYCow2xswF3ge+wZnnMBL4Me4jke6NpdAiIpI8UQcGAGvti8aYycDskLcOBh6K4BT/AkZZ\nazvMqJoC89J8fUke/W27Nv19kyjqPoagDxtzMfAE0a2+tgoYb63dHvOFRUQkaWLpY/Cz1i4DDgJ+\nRccjBCxOH8Moa+1IBQURkcwVV40h6ETG5AHfBYbhZE8tBOqBz4G3rbXbEnIhERFJqoQFhmxhjNkP\np6N8JDAQ2Am8AzxkrX01nWWT2BljKoAxwOnAkTgPJ63AZ8BLwGyNgOsajDE9gX8Cg7y7Jlhrn05f\nibqenAoMxpgjgNeAMu+ueqAnTpOaBW6x1s5MU/EkRt45MtUEj5KrB3oA+d7tOuBCa+0fU1w8STBj\nzEPA9QG7FBgSLK4+hmxijCkGXsQJCv8Ahllr9wX6AA/g3FTuNcacmb5SSox8N/9VwI+AUu/ftgQ4\nB6c5sw/wvLfGKFnKGPNd4Brg7XSXpSvLmcAAXIEzr2IXcJ619gMAa229tfYmnHkZoLkV2agOONo7\nsOE31to6AGttq7X2JZzg0Az0wvnvQLKQtx/zCe/mVeksS1eXS4GhyvtzibV2s8v793t/ftcYc3CK\nyiQJYK3daa0Nu6aHtfZftKd2j3oNEckY1wLDgcettW7rw0iC5ERgMMbsQ/sNYXWYw/6G0xENcFrS\nCyWp5kvjkt/hUZKRjDEDgf8CtgK3prk4XV5OBAbgENo7Jj9wO8Ba6wE+8m4emopCSWp4072f4N18\nP51lkZg9AuwD3GSt3dnZwRKfXAkM/QP+/UUHx/ne69/BMZJ9rgb2AzzAf6e5LBIlY8x5wGjgdWvt\nonSXJxfkSmDoEfDvpg6Oa/T+7JnEskgKeYco3+PdnOMbdCDZwRjTA5iDs6zw1WkuTs7IlcDgmg5c\nujZjTH+c0WYlOGuW35zeEkkM7gIqgFnW2g/TXZhckSuBYVfAv4vDHuXcQEKPlyxkjCnFWUt8f+Bj\n4NxIVhmUzGGMOQpnIlsNToCQFIkp7XYWCuxXGEB7J3OoAd6fSp2QxYwx++KMPhsGbATOsNZuTW+p\nJAazcUaRTQOMNxWGm+7e9zzW2sYwx0gUcqXG8C+clBcAh7kd4J088x3vpqqsWcrbJv0/OOPdt+AE\nhY3pLZXEqNL7879xFv4KffnM9W7r/9sEyYnAYK39Bljj3fxhmMO+T/vSo0qml4W8aU9+BxyPM2/h\nDGvtx+ktlUj2yYnA4LXE+7PK2ykZ6ibvz7XW2nBNTZKhjDHdgBXAqcAO4EyNQMpu1toh1loT7hVw\n6ATvviHpKmtXk0uB4QmcDJz7AL83xhwKzqxoY8wvcVI2A9ySpvJJjIwx+TiB/2ycJoUR1tq/p7dU\nItkrVzqfsdY2GWNG4TQTfRf4wBjjlnb75TQWU2JzAnCh99+FOFlUwx1bY639t5SUSiRL5UxgALDW\nrjPGDCN4oZ5anIV6ZmmhnqwVWPMt8r7C0ZBVkU7k1EI9IiLSuVzqYxARkQgoMIiISBAFBhERCaLA\nICIiQRQYREQkiAKDiIgEUWAQEZEgCgwiIhJEgUFERIIoMIiISBAFBhERCfL/AZmQsdF3V5mnAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130b5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 通过rcParams设置全局横纵轴字体大小\n",
    "mpl.rcParams['xtick.labelsize'] = 24\n",
    "mpl.rcParams['ytick.labelsize'] = 48\n",
    "np.random.seed(42)\n",
    "x = np.linspace(0, 5, 100)\n",
    "y = 2 * np.sin(x) + 0.3 * x ** 2\n",
    "# np.random.normal参数的意义为：\n",
    "# loc:float\n",
    "# 概率分布的均值，对应着整个分布的中心center\n",
    "# scale:float\n",
    "# 概率分布的标准差，对应于分布的宽度，scale越大越矮胖，scale越小，越瘦高\n",
    "# size:int or tuple of ints\n",
    "# 输出的shape，默认为None，只输出一个值\n",
    "# 我们更经常会用到np.random.randn(size)所谓标准正太分布（μ=0, σ=1），对应于np.random.normal(loc=0, scale=1, size)\n",
    "\n",
    "y_data = y + np.random.normal(scale=0.3, size=100)\n",
    "plt.figure('data')\n",
    "plt.plot(x, y_data, '.')\n",
    "plt.figure('model')\n",
    "plt.plot(x, y)\n",
    "# 两个图画在一起\n",
    "plt.figure('data & model')\n",
    "# k : 指定颜色，lw 指定宽度\n",
    "# 第三个参数除了颜色也可以指定线形，比如 'r--'表示红色虚线\n",
    "plt.plot(x, y, c='k', lw=3)\n",
    "plt.scatter(x, y_data, c='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CzLYu+YH6kKQG4E/AKomJQz5R39oyDvgF/Wge5yHzhj6y9st1fT65QBcWAN8b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\naiAcZhAEQRDUQDjMIAiCIKiB/wPWrHP/+fL+EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131a0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['axes.titlesize'] = 20\n",
    "mpl.rcParams['xtick.labelsize'] = 16\n",
    "mpl.rcParams['ytick.labelsize'] = 16\n",
    "mpl.rcParams['axes.labelsize'] = 16\n",
    "mpl.rcParams['xtick.major.size'] = 0\n",
    "mpl.rcParams['ytick.major.size'] = 0\n",
    "# 包含了狗、猫、猎豹的最高奔跑速度，还有对应的可视化颜色\n",
    "speed_map = {\n",
    "    'dog': (48, '#7199cf'),\n",
    "    'cat': (45, '#4fc4aa'),\n",
    "    'cheetah': (120, '#e1a7a2')\n",
    "}\n",
    "# 整体图的标题\n",
    "fig = plt.figure('Bar chart & Pie chart')\n",
    "ax = fig.add_subplot(121)\n",
    "ax.set_title('Running speed - bar chart')\n",
    "xticks = np.arange(3)\n",
    "bar_width = 0.5\n",
    "animals = speed_map.keys()\n",
    "speeds = [x[0] for x in speed_map.values()]\n",
    "colors = [x[1] for x in speed_map.values()]\n",
    "bars = ax.bar(xticks, speeds, width=bar_width, edgecolor='none')\n",
    "ax.set_ylabel('Speed(km/h)')\n",
    "ax.set_xticks(xticks + bar_width / 2)\n",
    "ax.set_xlabel(animals)\n",
    "ax.set_xlim([bar_width / 2 - 0.5, 3 - bar_width / 2])\n",
    "ax.set_ylim([0, 125])\n",
    "for bar, color in zip(bars, colors):\n",
    "    bar.set_color(color)\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.set_title('Running speed - pie chart')\n",
    "lables = ['{}\\n{}  km/h'.format(a, s) for a, s in zip(animals, speeds)]\n",
    "ax1.pie(speeds, labels=lables,colors=colors)\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
